This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. High-resolution Computed Tomography (HRCT) is frequently used to detect tumors in patients, and to monitor tumor growth or shrinkage at different time intervals during treatment. The accurate classification of a tumor into benign or malignant categories is critical to determine the appropriate treatment and CTs are often used to assess the effectiveness of a selected treatment. Advances in CT imaging technology have assisted in acquiring the images at increasingly high resolution;however, current algorithms are limited to measuring volume changes of the tumor rather than providing an accurate measurement of tumor growth in three dimensions. Of particular interest for this study are Ground-Glass Opacity (GGO) tumors that pose a special challenge to conventional image analysis algorithms, which are traditionally tuned toward detection of high gradient changes and thus would frequently miss GGO tumors. Ground-glass opacity refers to the appearance of a hazy opacity during high-resolution computed tomography (HRCT) that does not obscure the associated pulmonary vessels. This appearance results from parenchymal abnormalities that are below the spatial resolution of HRCT. In this study, we develop a novel three-dimensional (3D) method for interactive, automated and accurate segmentation and assessment of GGO tumors. The innovation of our method is the development of novel interactive 3D image analysis tool to extract GGO lung nodules, and perform analysis based on the resulting opacity map. To date, existing software algorithms are able to help detect and measure solid lung nodules based on available CT-image information;however, they are not capable of working on GGO tumors and estimating the overall GGO coverage of detected nodules in the lung. Current methods utilize manual expert analysis for this important task. We propose to measure quantitatively the opacity property of each pixel in a ground-glass opacity tumor from CT images. Our method results in an opacity map in which each pixel takes opacity value between 0-1. Given a CT image, we propose to accomplish the estimation by constructing a graph Laplacian matrix and solving a linear equations system, with assistance from some manually drawn scribbles for which the opacity values are easy to determine manually. The development of an automated GGO lung tumor detection will greatly improve the efficiency of routine radiological and oncological analysis. Our innovative approach for an objective assessment of GGO tumors will allow the radiologist or thoracic surgeon to evaluate the threedimensional evolution of the tumor and the dimensional changes detected by CT scans taken at different time spans, including changes in growth pattern, maximum areas/orientation of growth, and opacity changes. This proposed study is the first step toward the development of a computerized assessment of GGO tumors and, if successful, will lead to further translational efforts to integrate these techniques into clinical practice. The team brought together to successfully work on this effort is comprised of a thoracic surgeon, who acts as a clinical subject matter expert, and experienced researchers in image enhancement, automated vision and biomedical imaging.